Baseten Review

We evaluated Baseten across two use cases relevant to SMF Works' inference needs:

• HuggingFace Inference Provider integration — running GLM-5.2, DeepSeek V4 Flash, and Kimi K2.7 Code through Baseten's serverless Model APIs, authenticated via HuggingFace token
• Dedicated GPU deployment — provisioning H100 80GB replicas for sustained batch inference workloads

Testing period: September 2026, spanning approximately two weeks of daily use. Workloads included agent orchestration prompts (1-4K tokens), long-context document processing (30-100K tokens), and batch eval runs (sustained throughput over 2-4 hour windows).

HuggingFace integration is seamless. If you already have an HF account and PRO credits, running a model through Baseten requires zero setup beyond selecting it as your provider on the model page. The HF token authenticates, Baseten bills at provider rates with no HF markup, and the response format is standard. This is the lowest-friction path from "I found a model on HuggingFace" to "I'm calling it in production" that we have tested.

Inference speed is consistently fast. Baseten frequently tops the HuggingFace provider speed table. In our testing, GLM-5.2 returned 58-62 tok/s output, DeepSeek V4 Flash returned 95-110 tok/s, and Kimi K2.7 Code returned 28-32 tok/s. These numbers match or exceed DeepInfra and Fireworks on the same models. For latency-sensitive agent workloads, Baseten is among the fastest serverless options.

Truss framework for custom models. We did not test a custom model deployment in this round, but the Truss packaging format — config.yaml + model.py with load() and predict() — is a genuine differentiator. If your model needs custom preprocessing or non-standard architecture support, Truss gives you a structured way to deploy it that raw serverless providers do not offer. Standard HuggingFace models migrate in minutes according to Baseten's docs and third-party reports.

Observability is built in. Request-level tracing, latency percentiles, token usage breakdowns, and cost analytics are available in the dashboard without adding external tools. For teams that do not want to set up Langfuse separately, this is a real convenience.

Enterprise compliance. SOC 2, HIPAA, and private endpoints are available. SLA contracts for dedicated deployments. This matters for teams that cannot use shared serverless infrastructure for compliance reasons.

The dedicated GPU bill is steep for always-on workloads. Baseten's own production guidance recommends min_replicas >= 2. Two H100 80GB replicas at $6.50/hr each, running 24/7, costs $9,360/month. The same compute from a raw GPU rental provider (Spheron, Vast.ai) costs $1,400-2,000/month. You are paying a 4-6x premium for managed infrastructure, SLAs, and observability. For teams where that premium buys them not hiring an ML ops engineer, it is worth it. For teams that already have ops capacity, it is not.

Serverless cold starts are real. Model APIs scale to zero, and the first request after idle pays a 3-8 second cold-start penalty depending on model size. For interactive agent workloads where a user is waiting, this is noticeable. Keeping replicas warm moves you to dedicated pricing, which defeats the serverless cost advantage.

Narrower catalog than competitors. Baseten focuses on frontier open models — Kimi, DeepSeek, GLM, Nemotron, GPT-OSS. DeepInfra serves the same models plus a long tail of smaller and older models. If you need Llama 3.1 8B, Qwen3-4B, or niche fine-tunes, Baseten may not have them.

60% of revenue is from dedicated deployments. Baseten's product is optimized for the enterprise dedicated-GPU customer. Serverless Model APIs are a secondary offering. This shows in the UX: dedicated deployment configuration is well-documented and polished; serverless rate limiting and quota management feel like an afterthought.

No non-US data residency. All infrastructure is US-based. EU teams with GDPR data residency requirements cannot use Baseten without a data processing agreement, which is not standard.

Baseten is the right choice for teams that need production-grade inference of open-weight or custom models with SLAs, compliance guarantees, and built-in observability — and have the budget to pay a premium for managed infrastructure over raw GPU rental. Cursor, Notion, and Clay use it for exactly this reason.

It is the wrong choice for teams optimizing purely for cost-per-token (DeepInfra is cheaper), teams with broad model catalog needs (OpenRouter or DeepInfra are broader), or teams that already run their own GPU infrastructure and do not need the managed layer.

Rating: 3.8/5

Baseten is a well-engineered product that solves a real problem — production inference of open models with enterprise requirements — and charges accordingly. The HuggingFace integration is excellent, the speed is best-in-class, and the Truss framework is a genuine differentiator for custom models. The dedicated GPU pricing is the main drag on the score: it is 4-6x more expensive than raw GPU rental for always-on workloads, and the serverless cold-start penalty limits its usefulness for interactive applications. If your budget covers the premium and your workload needs the features, Baseten delivers. If you are cost-optimizing, look elsewhere.